Parametrization of the attainable set for a nonlinear control model of a biochemical process

  • Received: 01 October 2012 Accepted: 29 June 2018 Published: 01 June 2013
  • MSC : Primary: 49J15, 49N90; Secondary: 93C10, 93C95.

  • In this paper, we study a three-dimensional nonlinear model of a controllable reaction$ [X] + [Y] + [Z] \rightarrow [Z] $, where the reaction rate is given by a unspecifiednonlinear function. A model of this type describes a variety of real-life processes in chemicalkinetics and biology; in this paper our particular interests is in its application to wastewater biotreatment. For this control model, we analytically study the corresponding attainableset and parameterize it by the moments of switching of piecewise constant control functions.This allows us to visualize the attainable sets using a numerical procedure.
        These analytical results generalize the earlier findings, which were obtained for a trilinearreaction rate (which corresponds to the law of mass action) and reportedin [18,19], to the case of a general rate of reaction. These results allow toreduce the problem of constructing the optimal control to a straightforward constrained finitedimensional optimization problem.

    Citation: Ellina Grigorieva, Evgenii Khailov, Andrei Korobeinikov. Parametrization of the attainable set for a nonlinear control model of a biochemical process[J]. Mathematical Biosciences and Engineering, 2013, 10(4): 1067-1094. doi: 10.3934/mbe.2013.10.1067

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  • In this paper, we study a three-dimensional nonlinear model of a controllable reaction$ [X] + [Y] + [Z] \rightarrow [Z] $, where the reaction rate is given by a unspecifiednonlinear function. A model of this type describes a variety of real-life processes in chemicalkinetics and biology; in this paper our particular interests is in its application to wastewater biotreatment. For this control model, we analytically study the corresponding attainableset and parameterize it by the moments of switching of piecewise constant control functions.This allows us to visualize the attainable sets using a numerical procedure.
        These analytical results generalize the earlier findings, which were obtained for a trilinearreaction rate (which corresponds to the law of mass action) and reportedin [18,19], to the case of a general rate of reaction. These results allow toreduce the problem of constructing the optimal control to a straightforward constrained finitedimensional optimization problem.


    [1] Springer, Berlin-New York, 1984.
    [2] Computers & Chemical Engineering, 34 (2010), 802-811.
    [3] Springer-Verlag, Berlin-Heidelberg-New York, 2003.
    [4] Sarsia, 85 (2000), 211-225.
    [5] Environmental Technology Letters, 6 (1985), 467-476.
    [6] Mathematics-in-Industry Case Studies Journal, 2 (2010), 34-63.
    [7] Bulletin of Mathematical Biology, 52 (1990), 677-696.
    [8] Journal of Mathematical Sciences, 12 (1979), 310-353.
    [9] CRS Press, Boca Raton, Florida, 1994.
    [10] Nauka, Moscow, 1967.
    [11] SIAM Journal of Control and Optimization, 30 (1992), 1087-1091.
    [12] SIAM Journal on Applied Mathematics, 67 (2007), 337-353.
    [13] Computer Aided Chemical Engineering, 28 (2010), 967-972.
    [14] Moscow University. Computational Mathematics and Cybernetics, (2001), 27-32.
    [15] Dynamical Systems and Differential Equations, (2003), 359-364.
    [16] Moscow University. Computational Mathematics and Cybernetics, (2005), 23-28.
    [17] Journal of Dynamical and Control Systems, 11 (2005), 157-176.
    [18] Neural, Parallel, and Scientific Computations, 20 (2012), 23-35.
    [19] in "Industrial Waste" (eds. K.-Y. Show and X. Guo), InTech, Croatia, (2012), 91-120.
    [20] Journal of Theoretical Biology, 227 (2004), 349-358.
    [21] Differential Equations, 45 (2009), 1636-1644.
    [22] Journal of Applied Mathematics and Mechanics, 62 (1998), 169-175.
    [23] Jorn Wiley & Sons, New York-London-Sydney, 1964.
    [24] Giorn. Ist. Ital. Attuari, 7 (1936), 74-80.
    [25] Mathematical Notes, 37 (1985), 916-925.
    [26] Mathematical Medicine and Biology, 26 (2009), 309-321.
    [27] Mathematical Medicine and Biology, 26 (2009), 225-239.
    [28] Discrete and Continuous Dynamical System. Ser. B, 14 (2010), 1095-1103.
    [29] American Mathematical Society, Providence, RI, 1968.
    [30] Birkhäuser, Boston, 1997.
    [31] Mathematical Medicine and Biology, 27 (2010), 157-179.
    [32] Mathematical Biosciences and Engineering, 8 (2011), 307-323.
    [33] Journal of Mathematical Biology, 64 (2012), 557-577.
    [34] Jorn Wiley & Sons, New York, 1967.
    [35] Mat. Zametki, 41 (1987), 71-76, 121.
    [36] Control and Cybernetics, 36 (2007), 5-45.
    [37] Journal of Applied Mathematics and Mechanics, 46 (1982), 590-595.
    [38] Mathematical Notes, 27 (1980), 429-437, 494.
    [39] Journal of Optimization Theory and Applications, 64 (1990), 349-366.
    [40] Journal of Optimization Theory and Applications, 64 (1990), 367-377.
    [41] Springer-Verlag, Berlin-Heidelberg-New York, 1983.
    [42] Bulletin of the American Mathematical Society, 48 (1942), 883-890.
    [43] Springer, New York-Heidelberg-Dordrecht-London, 2012.
    [44] Hermann, Paris, 1967.
    [45] Computational Mathematics and Mathematical Physics, 47 (2007), 1768-1778.
    [46] Computational Mathematics and Mathematical Physics, 51 (2011), 537-549.
    [47] IEEE Transactions on Curcuits and Systems, 32 (1985), 503-505.
    [48] Discrete and Continuous Dynamical Systems. Ser. B, 3 (2003), 361-382.
    [49] SIAM Journal on Applied Mathematics, 61 (2000), 803-833.
    [50] SIAM Journal on Applied Mathematics, 61 (2000), 983-1012.
    [51] Springer-Verlag, Berlin-Heidelberg-New York, 1985.
    [52] Automation and Remote Control, 70 (2009), 772-786.
    [53] Automation and Remote Control, 72 (2011), 1291-1300.
    [54] Journal of Mathematical Sciences, 139 (2006), 6863-6901.
    [55] AMS, 2008.
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